Universal Computational Super-Resolution Framework for Imaging of Quantum Dots
Year: 2026
Type: article
Abstract: We present a universal deep learning method that reconstructs super-resolved images of quantum emitters from a single camera frame measurement. Trained on physics-based synthetic data spanning diverse point-spread functions, aberrations, and noise, the network generalizes across experimental conditions without system-specific retraining. We validate the approach on low- and high-density In(Ga)As quantum dots and strain-induced dots in 2D monolayer WSe2, resolving overlapping emitters even under low signal-to-noise ratios and inhomogeneous backgrounds. By eliminating calibration and iterative acquisitions, this single-shot strategy enables rapid, robust computational super-resolution for nanoscale characterization and quantum-photonic device fabrication.
Source: ACS Nanoscience Au
Institutions: Palacký University Olomouc, Stockholm University, Carl von Ossietzky Universität Oldenburg, University of Applied Sciences Emden Leer, University of Würzburg, Fraunhofer Institute for Applied Optics and Precision Engineering, Max Planck School of Photonics, Friedrich Schiller University Jena, Arizona State University
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,500
Funders: European Commission, Ministerstvo Školství, Mládeže a Tělovýchovy, Grantová Agentura České Republiky, Bundesministerium für Bildung und Forschung, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet, Univerzita Palackého v Olomouci, QuantERA, Horizon 2020 Framework Programme, Wallenberg Centre for Molecular and Translational Medicine, Wallenberg Center for Quantum Technology, Chalmers University of Technology
OpenAlex ID: W4414981738